Time series forecasting for stock market prediction through data discretization by fuzzistics and rule generation by rough set theory

Pal, Shanoli Samui; Kar, Samarjit

doi:10.1016/j.matcom.2019.01.001

Cited by 56 publications

(22 citation statements)

References 31 publications

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“…The accuracy of the forecast model seems upper than 51%, which illustrate 23% increase in prediction accuracy. Pal and Kar (2019) used a hybrid approach to forecast time series of stock price by using data discretization based on fuzzistics [1; 2], where cumulative probability distribution approach (CPDA) is used to get the intervals for the linguistic values. First-order fuzzy rule generation and reduction of rule sets by rough set theory have been performed.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models

Farahani

Hajiagha

2021

Soft Comput

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models

Farahani

Hajiagha

2021

Soft Comput

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“…[8] tarafından Avustralya'daki elektrik talebinin yarım saatlik bir ölçekte tahmin etme potansiyeli araştırılmıştır. Pal ve Kar [9] hisse senedi fiyatlarını tahmin etmek için bulanık mantık yöntemi ile kaba küme teorisinden oluşan melez bir model geliştirmişlerdir. Önerdikleri modeli mevcut modeller ile karşılaştırarak daha iyi sonuç verdiğini tespit etmişlerdir.…”

Section: Li̇teratur Taramasi (Literature Review)unclassified

“…Geliştirdikleri yöntemi gerçek hayatta uygulanabilir iki örnek üzerinde test ederek başarılı sonuçlar elde etmişlerdir. Bu çalışmalar incelendiğinde zaman serileri analizi tekniklerinin [2,4] ve meta-sezgisel algoritmaların [5][6][7][8][9][10] EET tahmininde yaygın bir şekilde kullanıldığı ve başarılı sonuçlar verdiği görülmüştür. Dünyada EET tahmininde yaygın olarak kullanılan bir diğer teknik ise YSA'dır.…”

Section: Li̇teratur Taramasi (Literature Review)unclassified

Jaya algoritması ile optimize edilmiş yapay sinir ağlarını kullanarak Türkiye’de elektrik enerjisi tüketiminin tahmini

Uzlu

Dede

2020

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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In this study, Turkey's electric energy consumption was predicted by using artificial neural networks with Jaya algorithm. As shown in Fig. A, three-layer ANN model encompassing GDP, population, and import-export values as entry parameters was formed with electric energy consumption as the exit parameter. Jaya algorithm was employed for network training. Figure A. Proposed ANN model for electric energy consumptionPurpose: The main objective of the present study was to apply ANN (artificial neural network) model with the Jaya algorithm to estimate electric energy consumption (EEC) in Turkey. Theory and Methods:The model estimates EEC based on gross domestic product (GDP), population, import, and export data. Data from 1980 to 2008 were used to develop the model and data from 2009 to 2014 were used to evaluate the model's predictive ability. ANN-Jaya model's performance was compared with the performance of artificial bee colony (ABC) and teaching learning based optimization (TLBO) algorithm-trained ANN models. After defining the best model according to various error criteria, EEC values for Turkey were predicted under two different scenarios.Results: Simulation results show that, ANN-Jaya modeling performed better results than ANN-TLBO or ANN-ABC modeling. According to the root mean square error values obtained for the test set, the proposed ANN-Jaya model performed 58.9% and 43.9% better than the ANN-ABC and ANN-TLBO models, respectively. Conclusion:In this study, the EEC in Turkey between 1980 and 2023 was modeled based on GDP, population, import, and export indicators under two different scenarios. The two scenarios showed that the future EEC of Turkey would vary between 311.5 and 333.6 TWh in 2023.

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“…Pawlak mainly based on the object between the indistinguishability of the theory of object clustering into basic knowledge domain, by using the basic knowledge of the upper and lower approximation [4] to describe the data object uncertainty, which derives the concept of classification or decision rule. Related researches spread many field, for instance, machine learning [5]- [10], cloud computing [11] [12] [13] [14], knowledge discovery [15] [16] [17] [18], biological information processing [19] [20], artificial intelligence [21] [22] [23] [24] [25], neural computing [26] [27] [28] and so on.…”

Section: Introductionmentioning

confidence: 99%

The Approach to Probabilistic Decision-Theoretic Rough Set in Intuitionistic Fuzzy Information Systems

Sang¹,

Zhang²

2020

IIM

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For the moment, the representative and hot research is decision-theoretic rough set (DTRS) which provides a new viewpoint to deal with decision-making problems under risk and uncertainty, and has been applied in many fields. Based on rough set theory, Yao proposed the three-way decision theory which is a prolongation of the classical two-way decision approach. This paper investigates the probabilistic DTRS in the framework of intuitionistic fuzzy information system (IFIS). Firstly, based on IFIS, this paper constructs fuzzy approximate spaces and intuitionistic fuzzy (IF) approximate spaces by defining fuzzy equivalence relation and IF equivalence relation, respectively. And the fuzzy probabilistic spaces and IF probabilistic spaces are based on fuzzy approximate spaces and IF approximate spaces, respectively. Thus, the fuzzy probabilistic approximate spaces and the IF probabilistic approximate spaces are constructed, respectively. Then, based on the three-way decision theory, this paper structures DTRS approach model on fuzzy probabilistic approximate spaces and IF probabilistic approximate spaces, respectively. So, the fuzzy decision-theoretic rough set (FDTRS) model and the intuitionistic fuzzy decision-theoretic rough set (IFDTRS) model are constructed on fuzzy probabilistic approximate spaces and IF probabilistic approximate spaces, respectively. Finally, based on the above DTRS model, some illustrative examples about the risk investment of projects are introduced to make decision analysis. Furthermore, the effectiveness of this method is verified.

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Time series forecasting for stock market prediction through data discretization by fuzzistics and rule generation by rough set theory

Cited by 56 publications

References 31 publications

Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models

Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models

Jaya algoritması ile optimize edilmiş yapay sinir ağlarını kullanarak Türkiye’de elektrik enerjisi tüketiminin tahmini

The Approach to Probabilistic Decision-Theoretic Rough Set in Intuitionistic Fuzzy Information Systems

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